In low and high income countries alike, disability exacerbates social, economic, and health disparities, in spite of their differences.
This study seeks to identify factors that predict the circumstances people with disabilities face, including poverty.
A cross-sectional study design was employed using census track level data for the cities of Monterrey, Nuevo Leon, and Dallas, Texas, from Mexico 2010 and USA 2000 census data collections. Two methods, spatial autocorrelation and geographically weighted regression were used to identify spatial patterns of disability and to explore the relation between disability and context-specific socio-demographic factors.
Results indicated that people with disabilities living below the poverty line experience high segregation levels in the semi-central zones of Dallas. In Monterrey, people with disabilities clustered in central areas of the city. A Geographically Weighted Regression (GWR) from both data analyses reported high goodness of fit (R ≥ 0.8 for Dallas data and R ≥ 0.7 for Monterrey data, respectively) and predictability of disability prevalence when social disadvantage factors such as unemployment, housing insecurity, household living conditions, and lack of education were present.
The divergent and sometimes conflicting trends in practices and policies addressing disability in low and high income environments renders a reexamination of the framework of disability. An understanding of local characteristics joins a grounded socio-cultural understanding of the various contexts that shape location-based social networks and political decisions in providing such an analysis.